Bibliographic record
Abstract
This article examines the rich historical subtext in the future-focused storylines of Quantic Dream’s 2018 release Detroit: Become Human (PS4) and illuminates many of the thematic continuities in racial issues between the past and the future. Much of the subtle historical symbolism appears to have went unnoticed by many reviewers who maligned the videogame and its creator David Cage for relying on lazy tropes that clunkily connect the African American civil rights movement to the narrative of woke androids engaging in a struggle for greater equality in society. Following scholarship that has examined the development of racialized thought in the past, this essay recognizes “race” as a powerful, yet malleable social construct, that sometimes changes over time. Racial concepts in the game do not perfectly align with historical or contemporary understandings of “race” in the United States. Androids, in short, all belong to the same “race.” This article then contends that the storylines of all three playable characters in the game resonate with well-crafted historical parallels and that the narrative geography in the gameworld often closely tethers to the historical geography of Detroit. The characters Markus, Connor, and Kara have intertwining stories that represent different elements of minority life in the United States with the clearest parallels to the historical experience of African Americans. Detroit: Become Human, nonetheless, is a science fiction game about androids. Framing the struggle for equal rights in the future with a group of beings that do not yet exist has the potential to disarm gameplayers of latent biases that may otherwise color their view of contemporary racial issues. The article asserts that the wedding together of past and future through experiential gameplay nurtures an empathic understanding of minority concerns that may carry over to the present to impact understandings of contemporary racial issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".